Robots, which have been used as industrial robots, are increasingly applied as intelligent robots that can be operated in any environment. Examples of the intelligent robots include a cleaning robot, a guard robot, a hazardous material detection robot, a domestic assistant robot, and an education robot. In order for such a robot to provide a service to people, robot navigation technologies need to be ensured. Among them, a self-localization technology for self-localization of a robot is particularly important.
Self-localization is based on a map, and accordingly various changes may be made in accordance with how the map representation to be used by the robot is constructed and what sensor is used. Recent development of associated technology focuses on a combination of a numerical map, such as a lattice map, and a topological map, and a vision sensor, instead of a laser or ultrasonic sensor. This is because the vision sensor is increasingly used in that it can provide abundant additional information, such as color, object, and human recognition, based on distance information and images.
In the related art, self-localization of a robot based on object recognition technology using a vision sensor is as follows.
Examples of the related art are Korean Patent Laid-open Application No. 10-2006-0102389, in which an active tag is attached to an object and is recognized by a vision sensor, to thereby obtain the positions of the object and the robot, and Korean Patent Laid-open Application No. 10-2005-0062713, a position control system of a mobile robot based on vision. However, in Korean Patent Laid-open Application No. 10-2006-0102389, it is inconvenient in that an artificial tag is attached to an object on a traveling path of a robot. In addition, in Korean Patent Laid-open Application No. 10-2005-0062713, a vision sensor is attached to the environment, not the robot, and accordingly the robot is only recognized, whereby position estimation is performed. For this reason, a vision sensor is needed in accordance with an environment, and as a result, a plurality of vision sensors.
Another example of the related art is U.S. Pat. No. 7,015,831 in which self-localization and map implementation are performed simultaneously. In U.S. Pat. No. 7,015,831, while recognition using a vision sensor is not performed in an object unit, three-dimensional local feature points are extracted, and self-localization is performed based on the extracted local feature points. Accordingly, there is no countermeasure against a subsequent change in the environment, and it becomes problematic, like a case where a laser sensor is used.
Examples of the papers as the related art are as follows.
Yuta et al have suggested a method that constructs a topological map around an object based on object recognition, and estimates the position of the robot by recognizing an object one by one and then recognizing another object while moving the robot (M. Tomoyo and S. Yuta, “Object-based Localization and Mapping Using Loop Constraints and Geometric Prior Knowledge,” International Conference on Robotics and Automation, pp. 862 to 867, 2003). This method is related to local localization tracking in a method for self-localization of a robot. However, there is a problem in that it may not be applied to global self-localization for new estimation of the position of the robot when the robot is unexpectedly moved to any position or when local position estimation is failed.
Park et al have suggested a method that represents an indoor environment as a topological map around a local area, recognizes an object in each local area to acquire a local area where the robot is located, and calculate the position of the robot from a local coordinate system defined in the local area (S. Park, M. Park, and S. K. Park, “Vision-based Global Localization for Mobile Robots with an Object Entity-based Hybrid Map,” International Conference on Advanced Robotics, pp. 761 to 766, 2007). In this method, the position of the robot is calculated by using only three-dimensional information of the recognized object. Accordingly, when the three-dimensional of the recognized object is insufficient, the position may be inaccurately calculated.